Development and Assessment of a Micro-CT Based System for Quantifying Loaded Knee Joint Kinematics and Tissue Mechanics
Bibliographic record
Abstract
Although anterior cruciate ligament (ACL) reconstruction is a highly developed surgical procedure, sub-optimal treatment outcomes persist. This can be partially attributed to an incomplete understanding of knee joint kinematics and regional tissue mechanic properties. A system for minimally-invasive investigation of knee joint kinematics and tissue mechanics under clinically relevant joint loads was developed to address this gap in understanding. A five degree-of-freedom knee joint motion simulator capable of dynamically loading intact human cadaveric knee joints to within 1% of user defined multi-axial target loads was developed. This simulator was uniquely designed to apply joint loads to a joint centered within the field of view of a micro-CT scanner. The use of micro-CT imaging and tissue-embedded radiopaque beads demonstrated high-resolution strain measurement, distinguishing differences in inter-bead distances as low as 0.007 mm. Inter-bead strain measurement was highly accurate and repeatable, with no significant error introduced from cyclic joint loading. Finally, regional strain was repeatably measured using radiopaque markers in four intact, human cadaveric knees to within 0.003 strain in response to multi-directional joint loads. This novel combination of dynamic knee joint motion simulation, tissue-embedded radiopaque markers, and micro-CT imaging provides the opportunity to increase our understanding of the kinematics and tissue mechanics of the knee, with the potential to improve ACL reconstruction outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".